ArticlePLoS computational biology2015
Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed.
- Estimating the Long-Term Clinical and Economic Impact of Universal Varicella Vaccination in Portugal Using a Dynamic Transmission Model.PharmacoEconomics - open · 2026Article
- A Modeling Approach to Inform In Vitro Hypoxia Platforms for Patient-Specific Analyses.Annals of biomedical engineering · 2026Article
- On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2.PLoS computational biology · 2026Article
- A model for the human fetal ventricular myocyte electrophysiology.PLoS computational biology · 2026Article
- Gaussian process emulation for exploring complex infectious disease models.PLoS computational biology · 2025Article
- Targeting alcohol use in high-risk population groups: a US microsimulation study of beverage-specific pricing policies.The Lancet. Public health · 2025Article
- Gaussian Process Emulation for Exploring Complex Infectious Disease Models.medRxiv : the preprint server for health sciences · 2025Article
- Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Emulating computer models with high-dimensional count output.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.Computers in biology and medicine · 2025Article
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability.bioRxiv : the preprint server for biology · 2024Article
- Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code.BMC medical research methodology · 2024Article
- New tuberculosis vaccines in India: modelling the potential health and economic impacts of adolescent/adult vaccination with M72/AS01BMC medicine · 2023Article
- First observation ofNature · 2023Article
- The importance of mechanical conditions in the testing of excitation abnormalities in a population of electro-mechanical models of human ventricular cardiomyocytes.Frontiers in physiology · 2023Article
- Inverse Generative Social Science: Backward to the Future.Journal of artificial societies and social simulation : JASSS · 2023Article
- When Is Mass Prophylaxis Cost-Effective for Epidemic Control? A Comparison of Decision Approaches.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Bayesian emulation and history matching of JUNE.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Complex model calibration through emulation, a worked example for a stochastic epidemic model.Epidemics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was 10(11) times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.